Big(ger) Data is Better

The power of big data is just starting to hit the marketplace. Though many organizations are still catching up with the tools they need to handle massive amounts of data easily, big data powered by analytics is providing organizations with better answers and improving intelligence in almost every industry – meeting customer needs, helping businesses predict repairs and identifying ways to cut costs.

More importantly, big data is changing lives.

From reducing infection in premature babies and more accurately signaling the early onset of sepsis, to evaluating drug safety and testing clinical interventions, big data is making a big impact.

Hospitals and academic medical centers are using big data and artificial intelligence to drive automation of tasks that are prone to human error, helping to optimize the patient experience, reduce treatment costs, gain new insights and improve decision support for clinicians that can immediately be used to improve patient care.

Big data is also being used to improve responsiveness, whether that is to care for patients or commercially, for the customer experience.

More data and analysis means a more accurate representation of reality, which means more context and more information on the dynamics, patterns, rare events, outliers, and trends in behaviors that can be the seeds of innovation and better outcomes. The ultimate promise of more data is better accuracy.

The International Organization for Migration is a United Nations agency that promotes international cooperation on migration issues and provides humanitarian assistance. By using big data analytics, relief workers in the field can accurately identify a country’s production capacities and inventories and match them with the most urgent service needs, ensuring that those most need of medicine, safety, housing and food receive it.

Identifying trends and patterns is also key to reducing cyberfraud. With online transaction fraud expected to balloon to more than $25 billion by 2020,[1] big data machine learning efforts can help banks improve their cyberfraud detection rates by up to 50 percent. These days, online transactions are part of many industry business models – online transaction fraud isn’t just a financial industry issue, it’s everyone’s issue.

Big data is also being used to develop better models for specific guidance, augmenting human expertise, or even automating repeated tactical scenarios. The greatest challenge in doing so is not just in training on the right data, but also identifying ways that the business processes can absorb the kinds of changes automation can bring.

This is happening in health care as it shifts to a focus on preventative care. The Cleveland Clinic is using big data and analytics to know who are its highest-risk patients, and how best to intervene with them to ensure a better health outcome. This requires it to gather social, demographic, and economic data and understand what works best for whom. For example, using big data and analytics, the Cleveland Clinic developed a model to identify which patients would recover from post-surgery care for knee replacement successfully at home instead of requiring post-acute care in a facility for all patients. The program not only better met patients’ needs, it also drove costs down.

But these advances don’t come easily. All data, big data included, can be “dirty” – full of errors, missing values, etc. It’s best to assume that data isn’t clean. Even with machine generated data – streaming from “things” in the IoT, the dynamics of the environment where the sensors are placed can affect readings, and interruptions in data transmission affect data completeness.

The cloud has brought big data within reach for almost any organization. For some, however, moving to the cloud hasn’t provided much solace for addressing complex big data questions because of the data preparation needed when multiple cloud sources are used. However, cloud data – like any other data – can be managed effectively and governed with a diligent strategy that examines all sources in context of their use.

What is big data and why now?

When we talk of big data having such an impact, what exactly are we talking about? It’s a big buzzword with a standardized definition – but simply put, it’s just more data that needs to be managed like any data.

The data isn’t different than what organizations had before, albeit some new sources are now accessible to many. The challenge is that there is a lot more of it, and it’s incoming at a greater pace. The enormous amount of data can make it time-consuming to compute a calculation or pull a report. Big data requires more horsepower and optimized analytics computing.

Though managing it can be daunting, more data is allowing all sorts of organizations – profit and not-for-profit, global enterprises and small businesses – to learn new things about their customers, their products and services, their priorities, and their contributions and value.

Big data is giving us better answers and thus helping organizations make better decisions and develop better solutions. And in many cases, it’s improving lives.

Fiona McNeill is a Global Product Marketing Manager at SAS, and co-author of The Heuristics in Analytics: A Practical Perspective of What Influences Our Analytical World. With a background in applying analytics to real-world business scenarios, she focuses on the automation of analytic insight in both business and application processing.She received her MA in Quantitative Behavioral Geography from McMaster University, examining inter-temporal time dependence in consumer purchasing behavior, and she graduated cum laude with a BSc in Bio-Physical Systems from the University of Toronto.

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